Reservoir Characterization of the
Benguela Belize Field, Offshore Angola, using Principle Component Analysis
By
Joao Nogueira1, Mark Moon2, John Dunn2
(1) Sonangol DPP, Luanda, Angola (2) ChevronTexaco Overseas Petroleum, San Ramon, CA
The Benguela Belize field is one of a series of sand filled turbidite
channel reservoirs that have been discovered in the deep waters of Block 14, off
the coast of Angola. With sparse well control and good 3D seismic,
characterization of the reservoir for detailed reservoir simulation has focused
on the use of seismic attribute
processing to help define channel geometries and
make rock property predictions.
This poster describes how Principle Component Analysis
(PCA), a long
established statistical technique used to transform and analyze multivariate
data sets, is being used in a new, innovative way to make better predictions of
rock properties, and help refine the definition of reservoir geometry and
quality. The process also addresses reservoir management issues such as,
water-flood sweep efficiency, well positioning, producing guidelines, and
completion tactics.
Predictions of Vshale using PCA were made using ChevronTexaco’s PCA proximity
transform algorithm. Which in basic terms takes seismic attribute
data such as
far-offset amplitude, and rearranges it into multi-dimensional principle
component space. The method presented combines well data with principle
components and uses an inverse distance interpolation to predict rock properties
at log resolution scale. In addition, clustering of data within PCA space
identifies geobodies which help to improve the imaging of the reservoir’s
complex amalgamated channel geometries. By integrating the cluster
analysis
and
proximity transform results into our reservoir model we were able to improve
definition of reservoir geometries and provide a good prediction of Vshale from
which porosity and permeability properties were populated using cloud
transforms.